Information Processing with Combination Method for Dependent Evidences Based on Principal Component Analysis

Abstract:

To meet the requirement that evidences must be independent for evidences combination in D-S evidence theory when the information processing, the dependence among evidences should be eliminated, so a new combination method of dependent evidences based on the Principal Component Analysis (PCA) is presented. The high-dimensional dependent evidences are replaced by the new low-dimensional independent evidences to reduce the dimensions following the guiding rule of PCA, and then the probability under the new evidences is calculated. The new independent evidences are combined with the combination rules of D-S evidence theory. Compared to existed methods, the dependence in initial evidences is eliminated, and the number of evidences is reduced, which leads to the simplification of the process of evidence combination. Finally, an example is employed to verify the feasibility and effectiveness of the proposed approach.

• Periodical: Advanced Materials Research (Volume 1022)
• Pages: 304-310
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1022.304
• Online since: August 2014
• Authors: Jie Yao*, Tao Hu, Jian Jun Yang
• Keywords: Dependent Evidence, Evidence Combination, Evidence Theory, PCA

References

[1] Yee Leung, Nan-nan JI, Jiang-hong MA. An integrated information fusion approach based on the theory of evidence and group decision-making[J]. Information Fusion, 2013(14), 410-422.

DOI: 10.1016/j.inffus.2012.08.002

[2] Xue-feng Xu, Gen-xiu Wu. A special combination method for dependent evidence [J]. Computer and Modernization, 2001(74): 14-18.

[3] Huai-jiang Sun, Jing-yu Yang. A combination method for dependent evidences [J]. Chinese Journal of Computers, 1999, 22(9): 1004-1007.

[4] Shan-lin Yang, Wei-dong Zhu, Ming-lun Ren. Combination theory and method for interrelated evidences based optimal adjustment coefficient [J]. Journal of Management Sciences in China, 2003, 6(5): 12-16.

[5] Zheng-cai Lu, Zheng Tan. General framework for evidence combiantion and its approach to highly conflicting evidence fusion [J]. Journal of Tsinghua University(Science and Technology), 2011, 51(11): 1611-1615.

[6] Rui-xia Zhang, Yong Wang. Intrusion detection methods based on PCA and multi-classifier fusion [J]. Microelectronics & Computer, 2010, 27(4): 191-192.

[7] Yue Sun. Study on Application of Information Integration and Reliable Data Processing Based on D-S Evidence Theory [D]. Chengdu: University of Electronic Science and Technology of China, (2011).

[8] Hervé Abdi, Lynne J. Williams. Principal component analysis[J]. Wiley interdisciplinary Reviews: Computational Statistics, 2010, 2(4), 433-459.

DOI: 10.1002/wics.101

[9] Peng-hua Wang, Yuan Li, Wen-ge Wang. Operational effectiveness of self-propelled gun system based PCA [J].

[10] Zeng-ming Chen. Study and Application of Evidence Theory in Group Decision-making [D]. Hefei: PhD Dissertation of Hefei University of Technology, 2006. Ordnance Industry Automation, 2009, 28(5): 24-25.